{"id":"W2800439477","doi":"10.1109/tii.2018.2832251","title":"HealthDep: An Efficient and Secure Deduplication Scheme for Cloud-Assisted eHealth Systems","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Cloud Data Security Solutions","field":"Computer Science","cited_by":216,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"National Key Research and Development Program of China; China Scholarship Council","keywords":"Cloud computing; eHealth; Data deduplication; Computer science; Confidentiality; Encryption; Scheme (mathematics); Computer security; Cloud storage; Server; Security analysis; Database; Computer network; Health care; Operating system; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002378231,0.0004828247,0.0005979192,0.0008674526,0.001105872,0.001262206,0.00165129,0.00066718,0.00159927],"category_scores_gemma":[0.0041963,0.000292658,0.0005019801,0.001002751,0.0006785202,0.003166466,0.002744292,0.0009318774,0.0006328307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008478081,"about_ca_system_score_gemma":0.001635834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007033334,"about_ca_topic_score_gemma":0.000656736,"domain_scores_codex":[0.9979004,0.0005067408,0.0002901078,0.0002551452,0.0008245933,0.0002229979],"domain_scores_gemma":[0.9953786,0.0007619871,0.0004131785,0.002461177,0.0008233233,0.0001616901],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00274812,0.0005552199,0.009728447,0.001139035,0.000369714,0.001319966,0.001028618,0.07826062,0.1083961,0.09919931,0.02990766,0.6673473],"study_design_scores_gemma":[0.000504549,0.001220563,0.006808151,0.0001702435,0.0002007667,0.004313682,0.000618164,0.7082553,0.1476804,0.04543246,0.08454067,0.000255059],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09013767,0.003333828,0.8950636,0.001149841,0.0005265356,0.001185259,0.0009740152,0.002213087,0.005416113],"genre_scores_gemma":[0.7698008,0.0009386683,0.2237353,0.0003707822,0.0002147493,0.0002268451,0.0009457768,0.00008101149,0.003686149],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002378231,"threshold_uncertainty_score":0.01257741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07556899879148138,"score_gpt":0.3146846274742344,"score_spread":0.239115628682753,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}